潜熱蓄熱技術を用いたEV用蓄電池保温に関する研究

大橋　達之・飯島　春幸（エフ・シー・シー）・能村　貴宏・中村　友一（北海道大学）

2050年のカーボンニュートラル実現に向け，EVの普及が進む中，寒冷地での使用に適した蓄電池の保温技術が求められています．私たちは，潜熱蓄熱技術と高断熱技術を用いたEV用蓄電池保温システムを研究しています．その潜熱蓄熱技術について報告します．

Development of Battery Data Generation Technology
Using Time Series Wasserstein GAN based on Deep Learning.

HYUNJUN JANG･TAEKYU KANG･WOOSUNG KIM (Hyundai Motor)

This paper presents a GAN-based deep learning approach for generating synthetic battery data. While lithium-ion batteries in xEVs are widely studied using deep learning to analyze their electrochemical characteristics under various conditions (current load, SOC, temperature), obtaining real data is challenging. Battery degradation testing is time-consuming, and extreme condition testing (like internal short circuits) is dangerous. The proposed solution uses a GAN architecture where Generator and Discriminator networks compete to create new, realistic battery data that matches the patterns of existing data, enabling testless data generation for battery research.

走行中給電による省エネ，省資源社会への貢献

加藤　直也・光田　徹治・谷　恵亮・大杉　拓也・山口　浩司・紺野　由希・石原　光浩・永松　敏樹・伊藤　淳貴・大島　圭市（デンソー）

カーボンニュートラル実現には，電気自動車の製造時と廃却時に発生する炭素の低減が重要である．著者らは，搭載バッテリー容量を大幅に減らすポテンシャルを持つ走行中給電システムの実現仕様を検討し，省エネルギーと省資源の効果を試算したので報告する．

Early fault detection in lithium-ion batteries using machine learning

Maximilian Kloock･Ethelbert Ezemobi･Seyedmehdi Hosseininasab･Lennart Bauer (FEV Europe)

Major battery faults include soft and hard short circuits, abnormal aging, over-charging, and over-discharging. Early detection of these faults can help to mitigate against thermal runaway. This work demonstrates a machine learning algorithm designed to detect these faults in an early stage and, consequently, prevent severe damage through thermal runaway. The machine learning algorithm combines the advantages of data-driven and model-based approaches. The algorithm is validated under dynamic load conditions using standard driving profiles of 100 simulated battery packs - each pack consisting of 80 cells, each including one faulty cell.